Identification of Circulating Endocan-1 and Ether Phospholipids as Biomarkers for Complications in Thalassemia Patients
Bibliographic record
Abstract
Despite advances in our knowledge and attempts to improve therapies, β-thalassemia remains a prevalent disorder with increased risk for the development of cardiomyopathy. Using an untargeted discovery-based lipidomic workflow, we uncovered that transfusion-dependent thalassemia (TDT) patients had a unique circulating lipidomic signature consisting of 387 lipid features, allowing their significant discrimination from healthy controls (Q-value < 0.01). In particular, TDT patients had elevated triacylglycerols and long-chain acylcarnitines, albeit lower ether phospholipids or plasmalogens, sphingomyelins, and cholesterol esters, reminiscent of that previously characterized in cardiometabolic diseases resulting from mitochondrial and peroxisomal dysfunction. Discriminating lipid (sub)classes correlated differentially with clinical parameters, reflecting blood (ether phospholipids) and iron (cholesterol ester) status or heart function (triacylglycerols). We also tested 15 potential serum biomarkers related to cardiometabolic disease and found that both lipocalin-2 and, for the first time, endocan-1 levels were significantly elevated in TDT patients and showed a strong correlation with blood parameters and three ether diacylglycerophosphatidylcholine species. In conclusion, this study identifies new characteristics of TDT patients which may have relevance in developing biomarkers and therapeutics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".